摘要：By use of semantic attributes of 3D object, the user can search for targeted objects, which main advantage is that it does not require the user to sketch a 3D object as the query for 3D object retrieval, and the retrieval system can obtain a better retrieval performance. There are many categorical datum among these attributes, and how to use those and find the most similar objects is a vital problem to resolve. However, several elements with different types may have a shorter Euclidean distance. It is obvious the objects belonging to the same category are closer. Therefore, we present a 3D object retrieval method with clustering principle and RBF interpolator, which need a robust clustering method. The k-modes is a classic clustering algorithm for categorical data set. Its principle is simple, but it is easy to converge to a local optimum. PSO (Particle Swarm Optimization) algorithm is an effective tool for optimization, so we attempt to overcome the local optimum problem with PSO for categorical data set. PSO usually used to solve continuous optimization problems., but the categorical data are non-continuous. This paper presents an a novel k-p-modes algorithm to overcome these problems. Results show the method is effective.
